Measurement of Air Pollution Parameters in Montenegro Using the Ecomar System

Particulate matter air pollution is one of the most dangerous pollutants nowadays and an indirect cause of numerous diseases. A number of these consequences could possibly be avoided if the right information about air pollution were available at a large number of locations, especially in urban areas. Unfortunately, this is not the case today. In the whole of Europe, there are just approximately 3000 automated measuring stations for PM10, and only about 1400 stations equipped for PM2.5 measurement. In order to improve this issue and provide availability of real-time data about air pollution, different low-cost sensor-based solutions are being considered both on-field and in laboratory research. In this paper, we will present the results of PM particle monitoring using a self-developed Ecomar system. Measurements are performed in two cities in Montenegro, at seven different locations during several periods. In total, three Ecomar systems were used during 1107 days of on-field measurements. Measurements performed at two locations near official automated measuring stations during 610 days justified that the Ecomar system performance is satisfying in terms of reliability and measurement precision (NRMSE 0.33 for PM10 and 0.44 for PM2.5) and very high in terms of data validity and operating stability (Ecomar 94.13%–AMS 95.63%). Additionally, five distant urban/rural locations with different traffic, green areas, and nearby industrial objects were utilized to highlight the need for more dense spatial distributions of measuring locations. To our knowledge, this is the most extensive study of low-cost sensor-based air quality measurement systems in terms of the duration of the on-field tests in the Balkan region.

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Design of an IoT based Real Time Environment Monitoring System using Legacy Sensors

MATEC Web of Conferences ◽

10.1051/matecconf/201821003008 ◽

2018 ◽

Vol 210 ◽

pp. 03008

Author(s):

Aparajita Das ◽

Manash Pratim Sarma ◽

Kandarpa Kumar Sarma ◽

Nikos Mastorakis

Keyword(s):

Air Pollution ◽

Light Intensity ◽

Real Time ◽

Environmental Conditions ◽

Low Cost ◽

Environment Monitoring ◽

Time Data ◽

Graphical Information ◽

Real Time Data ◽

Sun Light

This paper describes the design of an operative prototype based on Internet of Things (IoT) concepts for real time monitoring of various environmental conditions using certain commonly available and low cost sensors. The various environmental conditions such as temperature, humidity, air pollution, sun light intensity and rain are continuously monitored, processed and controlled by an Arduino Uno microcontroller board with the help of several sensors. Captured data are broadcasted through internet with an ESP8266 Wi-Fi module. The projected system delivers sensors data to an API called ThingSpeak over an HTTP protocol and allows storing of data. The proposed system works well and it shows reliability. The prototype has been used to monitor and analyse real time data using graphical information of the environment.

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Accuracy of the Dynamic Acoustic Map in a Large City Generated by Fixed Monitoring Units

Sensors ◽

10.3390/s20020412 ◽

2020 ◽

Vol 20 (2) ◽

pp. 412 ◽

Cited By ~ 9

Author(s):

Roberto Benocci ◽

Chiara Confalonieri ◽

Hector Eduardo Roman ◽

Fabio Angelini ◽

Giovanni Zambon

Keyword(s):

Traffic Flow ◽

Urban Areas ◽

Low Cost ◽

Traffic Noise ◽

Large City ◽

Field Measurements ◽

Life Project ◽

Noise Data ◽

Comparable Accuracy ◽

Monitoring Stations

DYNAMAP, a European Life project, aims at giving a real image of the noise generated by vehicular traffic in urban areas developing a dynamic acoustic map based on a limited number of low-cost permanent noise monitoring stations. The system has been implemented in two pilot areas located in the agglomeration of Milan (Italy) and along the Motorway A90 (Rome-Italy). The paper reports the final assessment of the system installed in the pilot area of Milan. Traffic noise data collected by the monitoring stations, each one representative of a number of roads (groups) sharing similar characteristics (e.g., daily traffic flow), are used to build-up a “real-time” noise map. In particular, we focused on the results of the testing campaign (21 sites distributed over the pilot area and 24 h duration of each recording). It allowed evaluating the accuracy and reliability of the system by comparing the predicted noise level of DYNAMAP with field measurements in randomly selected sites. To this end, a statistical analysis has been implemented to determine the error associated with such prediction, and to optimize the system by developing a correction procedure aimed at keeping the error below some acceptable threshold. The steps and the results of this procedure are given in detail. It is shown that it is possible to describe a complex road network on the basis of a statistical approach, complemented by empirical data, within a threshold of 3 dB provided that the traffic flow model achieves a comparable accuracy within each single groups of roads in the network.

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Smart Environmental Monitoring System

International Journal of Green Computing ◽

10.4018/ijgc.2019010103 ◽

2019 ◽

Vol 10 (1) ◽

pp. 43-54

Author(s):

Karthik Sudhakaran Menon ◽

Brinzel Rodrigues ◽

Akash Prakash Barot ◽

Prasad Avinash Gharat

Keyword(s):

Air Pollution ◽

Real Time ◽

Urban Areas ◽

Real Life ◽

Cloud Service ◽

Threshold Value ◽

Time Data ◽

Multiple Sensors ◽

Alert Message ◽

Environmental Monitoring System

In today's world, air pollution has become a common phenomenon everywhere, especially in the urban areas, air pollution is a real-life problem. In urban areas, the increased number of hydrocarbons and diesel vehicles and the presence of industrial areas at the outskirts of the major cities are the main causes of air pollution. The problem is seriously intense within the metropolitan cities. The governments around the world are taking measure in their capability. The main aim of this project is to develop a system which may monitor and measure pollutants in the air in real time, tell the quality of air and log real-time data onto a remote server (Cloud Service). If the value of the parameters exceeds the given threshold value, then an alert message is sent with the GPS coordinates to the registered number of the authority or person so necessary actions can be taken. The Arduino board connects with Thingspeak cloud service platform using ESP8266 Wi-Fi module. The device uses multiple sensors for monitoring the parameters of the air pollution like MQ-135, MQ-7, DHT-22, sound sensor, LCD.

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Smart Air Quality Monitoring and Sensing Device (SAQM Sensing Device)

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.37857 ◽

2021 ◽

Vol 9 (8) ◽

pp. 2787-2792

Author(s):

Hareetaa Mallani

Keyword(s):

Air Pollution ◽

Urban Areas ◽

Heart Diseases ◽

Low Cost ◽

Power Requirement ◽

Premature Deaths ◽

Response Recovery ◽

Alert Message ◽

Mobile Measurement ◽

Harmful Effects

Abstract: Air pollution is the biggest problem of every nation, whether it is developed or developing. Health problems have been growing at faster rate especially in urban areas of developing countries where industrialization and growing number of vehicles leads to release of lot of gaseous pollutants. Harmful effects of pollution include mild allergic reactions such as irritation of the throat, eyes and nose as well as some serious problems like bronchitis, heart diseases, pneumonia, lung and aggravated asthma. According to a survey, due to air pollution 50,000 to 100,000 premature deaths per year occur in the U.S. alone. LPG sensor is added in this system which is used mostly in houses. The system will show temperature and humidity. The system can be installed anywhere but mostly in industries and houses where gases are mostly to be found and gives an alert message when the system crosses threshold limit. The advantages of the detector, have a reliable stability, rapid response recovery and long-life features. It is affordable, userfriendly, low-cost and minimum-power requirement hardware which is appropriate for mobile measurement, as well as comprehensible data collection

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Geotechnical Challenges of Tehran Metro Line 7 (South Northern Route)

Civil Engineering Journal ◽

10.28991/cej-0309161 ◽

2018 ◽

Vol 4 (5) ◽

pp. 1117

Author(s):

Farzad Sadjadi ◽

Ahad Bagherzadeh Khalkhali

Keyword(s):

Groundwater Level ◽

Urban Areas ◽

Laboratory Tests ◽

Alluvial Soil ◽

Field Tests ◽

Geological Hazards ◽

Human Presence ◽

Level Of Difficulty ◽

The Right

Tunneling in urban areas, has raised the level of difficulty and challenge in respecting the constraints deriving from human presence and, therefore, the necessity for the study of geological and geotechnical properties and parameter, classification of the soils according to their engineering behavior, choosing the right TBM, determine groundwater level and determining possible geological hazards. In this paper some geological and geotechnical study took place along the tunnel route. This investigation is done by the result of 73 machinery borehole and 32 manual borehole that took place in the process of studying the tunnel route and continued by the result of field tests and laboratory tests and according to the result, the geological zone classified in 6 zone in tunnel route; due to the result of Cerchar abrasivity test and since Alluvial soil is the main soil in most of the tunneling route, the excavation soil classified as highly abrasive. In some part of tunneling there is a risk of clogging due to the high amount of clay. Based on the results of Lofran tests the permeability of most of the classified soils in route of the tunnel was obtained less that 10E-7 m/s.

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Assessment of heterogenity of air pollution within an urban canopy

10.5194/egusphere-egu2020-2822 ◽

2020 ◽

Author(s):

Vivien Voss ◽

K. Heinke Schlünzen ◽

David Grawe

Keyword(s):

Air Pollution ◽

Air Quality ◽

Citizen Science ◽

Urban Areas ◽

Hot Spot ◽

Low Cost ◽

Urban Canopy ◽

Limit Values ◽

Data Standard ◽

The City

Air pollution is an important topic within urban areas.&#160; Limit values as given in the European Guidelines are introduced to reduce negative effects on humans and vegetation.&#160; Exceedances of the limit values are to be assessed using measurements.&#160; In case of found exceedances of the limit values, the local authorities need to act to reduce pollution levels. Highest values are found for several pollutants (NOx, NO2, particles) within densely build-up urban areas with traffic emissions being the major source and dispersion being very much impacted by the urban structures.&#160; The quality assured measuring network used by the authorities is often too coarse to determine the heterogeneity in the concentration field. Low cost sample devices as employed in several citizen science projects might help to overcome the data sparsity. Volunteers measure the air quality at many sites, contribute to the measurement networks and provide the data on the web. However, the questions arising are: a) Are these data of sufficient high quality to provide results comparable to those of the quality assured networks? b) Is the network density sufficient to determine concentration patterns within the urban canopy layer? One-year data from a citizen science network, which measures particulate matter (PM10, PM2.5) were compared to measurements provided by the local environmental agency, using two hot-spot areas in the city of Hamburg as an example. To determine how well the measurements agree with each other, a regression analyses was performed dependent on seasonal and diurnal cycles. Additionally, model simulations with the microscale obstacle resolving model MITRAS were performed for two characteristic building structures and different meteorological situations. The model results were used to determine local hot spots as well as areas where measurements might represent the concentration of particles for the urban quarter. The low cost sensor measurements show a general agreement to the city&#8217;s measurements, however, the values per sensor differ. Moreover, the measurements of the low-cost-sensor show an unrealistic dependence on relative humidity, resulting in over- or underestimations in certain cases. The model results clearly show that only a few sites allow measurements to be representative for a city quarter. The measurements of the citizen science project can provide a good overview about the tendencies of the air quality, but are currently not of sufficient quality to provide measurements calling for legal action.The model results were used for the project AtMoDat. AtMoDat is an attempt to create a data standard for obstacle resolving models based on the existing Climate and Forecast (CF) conventions. A web-based survey is developed to get information on the requirements for the data standard. The next step is to extend the collection of model characteristics and eventually to provide a generic scheme.Acknowledgements This work contributes to project &#8220;AtMoDat&#8221; funded by the Federal Ministry of Education and Research under the funding number 16QK02C. Responsibility for the content of this publication lies with the authors.

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Quantification of sewer leakage by a continuous tracer method

Water Science & Technology ◽

10.2166/wst.2011.639 ◽

2011 ◽

Vol 64 (1) ◽

pp. 132-138 ◽

Cited By ~ 1

Author(s):

V. Prigiobbe ◽

M. Giulianelli

Keyword(s):

Urban Areas ◽

Structural Damage ◽

Laboratory Tests ◽

Low Cost ◽

Field Measurements ◽

Tracer Method ◽

Catchment Scale ◽

Chemical Tracers ◽

Challenging Tasks ◽

Dry Weather Flow

Water authorities interested in the evaluation of the structural state of a sewer must quantify leakage to plan strategic intervention. However, the quantification of the exfiltration and the localisation of structural damage are challenging tasks that usually require expensive and time-consuming inspections. Herein, we report one of the first applications of the QUEST-C method to quantify the exfiltration in a continuously operating sewer by dosing two chemical tracers, sodium bromide (NaBr) and lithium chloride (LiCl). The method was applied at the catchment scale in a 14-year-old sewer in Rome, Italy. Preliminary laboratory tests, field measurements, and numerical simulations showed that reliable results require the QUEST-C method to be applied to sewers without lateral inflows, during periods of quasi-steady flow, and that the travel time of the NaBr tracer is minimised. Three sewer reaches were tested and the estimated exfiltration, as a fraction of the dry weather flow (DWF), increased from 0.128 in the agricultural area to 0.208 in the urban area. Although our estimates are at the lower end of the range given in the literature (0.01–0.56 DWF), the exfiltration was not negligible, and interventions should focus on the sewers in urban areas. This illustrates the capability of the QUEST-C method to guide strategic intervention at low cost and without an interruption of sewer operation. However, careful interpretation of the results is recommended for sewers with many lateral inflows, where leakage may be overestimated.

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Multi-Horizon Air Pollution Forecasting with Deep Neural Networks

Sensors ◽

10.3390/s21041235 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1235

Author(s):

Mirche Arsov ◽

Eftim Zdravevski ◽

Petre Lameski ◽

Roberto Corizzo ◽

Nikola Koteli ◽

...

Keyword(s):

Air Pollution ◽

Urban Areas ◽

Short Term Memory ◽

Moving Average ◽

Arima Model ◽

Measurement Data ◽

Quality Measurement ◽

Capital City ◽

Industrial Plants ◽

Proactive Measures

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures.

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Low cost sensors and crowd-sourced data to map air pollution in urban areas

10.5194/egusphere-egu2020-18946 ◽

2020 ◽

Author(s):

Rodrigo Carbajales ◽

Massimiliano Iurcev ◽

Paolo Diviacco

Keyword(s):

Air Pollution ◽

Real Time ◽

Urban Areas ◽

Low Cost ◽

Reliable Data ◽

Crowd Sourcing ◽

Moving Vehicle ◽

Redundant Data ◽

Boundary Definition ◽

Easy Integration

Low cost sensors and crowd-sourcing data could potentially revolutionise the way air pollution measurements are collected providing high density geolocated data. In fact, so far data have been collected mostly using dedicated fixed position monitoring stations. These latter rely on high quality instrumentation, well established practices and well trained personnel, which means that, due to its costs, this paradigm entails limitations in the resolution and extension of geographic sampling of an area.The combination of low costs sensors and volunteer-based or opportunistic acquisition of data can, instead, possibly turn the cost issue into an advantage. This approach, however, introduces other limitations since low cost sensors provide less reliable data and crowd source acquisition are subjects to data gaps in space and time.In order to overcome these issues redundant data from multiple platforms have to be made available. On one hand this allows statistics to be applied to identify and remove anomalous values, and on the other hand when multiple platforms are used, the chances to have a better coverage and more reliable data&#160; increases.To implement this approach OGS developed the full suite of tools that has been named COCAL that allow to follow the full path from the acquisition, transmission, storage, integration and real time visualization of the crowdsourced data.Low cost sensors for the detection of suspended particulate matter size 2.5 and 10 &#181;m, together with atmospheric pressure, humidity and temperature, have been combined with GPS positioning and transmission (being able to opt for GSM, WiFi or LoRaWAN transmission) unit in a black box that can be attached to any moving vehicle travelling in an area. This way large areas can be sampled with high geographic resolution.Atmospheric data are collected in an InfluxDB database, which allows easy integration with TheThingsNetwork for LoRaWAN network management and directly with GSM and WiFi connections. Public users are provided with a real-time web interface based on OpenLayers for map visualization. Server based processing and conversion scripts generate both filtered data and aggregate data, by computing averages on a spatial and temporal grid.. Finally, automatic interpolation techniques like Inverse Distance Weighting or Natural Neighbours may provide detailed online maps with contouring and boundary definition. All products are available in near real-time through OGC compliant web services, suited for an easy integration with other repositories and services.

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Implementation and Performance of a Deeply-Coupled GNSS Receiver with Low-Cost MEMS Inertial Sensors for Vehicle Urban Navigation

Sensors ◽

10.3390/s20123397 ◽

2020 ◽

Vol 20 (12) ◽

pp. 3397 ◽

Cited By ~ 1

Author(s):

Xin Feng ◽

Tisheng Zhang ◽

Tao Lin ◽

Hailiang Tang ◽

Xiaoji Niu

Keyword(s):

Urban Areas ◽

Weight Control ◽

Low Cost ◽

Field Tests ◽

Urban Environments ◽

Global Navigation Satellite Systems ◽

Measurement Unit ◽

Positioning Accuracy ◽

Gnss Receiver ◽

Tracking Loops

In urban environments, Global Navigation Satellite Systems (GNSS) signals are frequently attenuated, blocked or reflected, which degrades the positioning accuracy of GNSS receivers significantly. To improve the performance of GNSS receiver for vehicle urban navigation, a GNSS/INS deeply-coupled software defined receiver (GIDCSR) with a low cost micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) ICM-20602 is presented, in which both GPS and BDS constellations are supported. Two key technologies, that is, adaptive open-close tracking loops and INS aided pseudo-range weight control algorithm, are applied in the GIDCSR to enhance the signal tracking continuity and positioning accuracy in urban areas. To assess the performance of the proposed deep couple solution, vehicle field tests were carried out in GNSS-challenged urban environments. With the adaptive open-close tracking loops, the deep couple output carrier phase in the open sky, and improved pseudo-range accuracy before and after GNSS signal blocked. Applying the INS aided pseudo-range weight control, the pseudo-range gross errors of the deep couple decreased caused by multipath. A popular GNSS/INS tightly-coupled vehicle navigation kit from u-blox company, M8U, was tested side by side as benchmark. The test results indicate that in the GNSS-challenged urban areas, the pseudo-range quality of GIDCSR is at least 25% better than that of M8U, and GIDCSR’s horizontal positioning results are at least 69% more accurate than M8U’s.

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